Better dimensional asserting

codes/models/gpt_voice/unified_voice.py

@@ -143,6 +143,10 @@ class UnifiedGptVoice(nn.Module):
         mel_inputs:  long tensor, (b,m)
         wav_lengths: long tensor, (b,)
         """
+        assert self.max_symbols_per_phrase >= mel_inputs.shape[1]
+        assert self.max_symbols_per_phrase >= text_inputs.shape[1]
+        assert self.max_total_tokens >= mel_inputs.shape[1] + text_inputs.shape[1]
+
         mel_inputs = self.set_mel_padding(mel_inputs, wav_lengths)
         speech_conditioning_input = self.randomly_permute_conditioning_input(speech_conditioning_input)
         speech_conditioning_input = self.conditioning_encoder(speech_conditioning_input).unsqueeze(1)
@@ -168,6 +172,8 @@ class UnifiedGptVoice(nn.Module):
         Performs autoregressive modeling on only text. Still requires a speech_conditioning_input due to the way the
         model inputs are formatted. Just provide any audio clip (arguably, zeros could be provided).
         """
+        assert self.max_symbols_per_phrase >= text_inputs.shape[1]
+
         speech_conditioning_input = self.randomly_permute_conditioning_input(speech_conditioning_input)
         speech_conditioning_input = self.conditioning_encoder(speech_conditioning_input).unsqueeze(1)
 
@@ -181,6 +187,8 @@ class UnifiedGptVoice(nn.Module):
         """
         Performs autoregressive modeling on only speech data.
         """
+        assert self.max_symbols_per_phrase >= mel_inputs.shape[1]
+
         mel_inputs = self.set_mel_padding(mel_inputs, wav_lengths)
         speech_conditioning_input = self.randomly_permute_conditioning_input(speech_conditioning_input)
         speech_conditioning_input = self.conditioning_encoder(speech_conditioning_input).unsqueeze(1)